from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HpMatchReporting(against_lib="sklearnex", config="config.yml")
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

WARNING! Mismatch between validation scores for 14 predictions (58.33%). See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.663	0.687	0.024
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.757	0.742	0.015
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.882	0.875	0.007
8	KNeighborsClassifier_brute_force	predict	100000	1	100	1.000	0.000	1.000
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.882	0.875	0.007
11	KNeighborsClassifier_brute_force	predict	100000	1	100	1.000	0.000	1.000
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.757	0.742	0.015
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.663	0.687	0.024
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.896	0.967	0.071
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.922	0.974	0.052
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.929	0.975	0.046
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.929	0.975	0.046
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.922	0.974	0.052
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.896	0.967	0.071

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	6.961	0.0	-1	1	0.049	0.004	0.235	0.236	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.000	0.0	-1	5	0.048	0.001	0.241	0.241	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.851	0.0	1	100	0.048	0.001	0.242	0.242	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.001	6.549	0.0	-1	100	0.048	0.000	0.255	0.255	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.000	6.158	0.0	1	5	0.047	0.001	0.274	0.274	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.017	0.0	1	1	0.048	0.000	0.238	0.238	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.359	0.0	-1	1	0.009	0.000	0.505	0.505	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.367	0.0	-1	5	0.009	0.000	0.512	0.512	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.374	0.0	1	100	0.009	0.000	0.498	0.499	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.363	0.0	-1	100	0.009	0.000	0.510	0.510	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.362	0.0	1	5	0.009	0.000	0.516	0.516	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.363	0.0	1	1	0.009	0.000	0.519	0.519	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.986	0.161	0.000	0.002	-1	1	0.172	0.004	11.518	11.522	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.025	0.003	0.000	0.025	-1	1	0.009	0.000	2.858	2.858	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.795	0.034	0.000	0.003	-1	5	0.174	0.004	16.108	16.112	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.026	0.003	0.000	0.026	-1	5	0.009	0.000	2.845	2.846	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.067	0.005	0.000	0.002	1	100	0.211	0.004	9.776	9.778	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.001	0.000	0.022	1	100	0.009	0.000	2.557	2.558	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.801	0.044	0.000	0.003	-1	100	0.211	0.004	13.290	13.292	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.026	0.003	0.000	0.026	-1	100	0.009	0.001	2.913	2.927	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.055	0.003	0.000	0.002	1	5	0.171	0.000	11.989	11.989	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.000	0.000	0.022	1	5	0.009	0.000	2.516	2.518	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.207	0.009	0.001	0.001	1	1	0.171	0.001	7.050	7.050	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.001	0.000	0.021	1	1	0.009	0.000	2.383	2.383	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.786	0.044	0.000	0.002	-1	1	0.026	0.000	68.969	68.970	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.009	0.005	0.000	0.009	-1	1	0.001	0.000	12.760	12.851	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.656	0.025	0.000	0.003	-1	5	0.027	0.001	96.934	97.005	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.008	0.006	0.000	0.008	-1	5	0.001	0.000	11.472	11.519	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.977	0.003	0.000	0.002	1	100	0.060	0.002	32.786	32.801	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	100	0.001	0.000	4.034	4.050	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.686	0.026	0.000	0.003	-1	100	0.060	0.000	44.994	44.995	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.007	0.004	0.000	0.007	-1	100	0.001	0.000	9.123	9.198	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.969	0.004	0.000	0.002	1	5	0.027	0.000	73.014	73.017	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	5	0.001	0.000	4.490	4.511	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.096	0.005	0.000	0.001	1	1	0.026	0.000	42.356	42.357	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.751	2.768	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

WARNING! Mismatch between validation scores for 12 predictions (50.0%). See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.929	0.910	0.019
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.946	0.941	0.005
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.951	0.940	0.011
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.951	0.940	0.011
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.946	0.941	0.005
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.929	0.910	0.019
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.891	0.879	0.012
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.911	0.905	0.006
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.894	0.917	0.023
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.894	0.917	0.023
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.911	0.905	0.006
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.891	0.879	0.012

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.261	0.061	0.025	0.0	-1	1	0.771	0.103	4.231	4.268	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.630	0.069	0.022	0.0	-1	5	0.743	0.015	4.889	4.890	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.622	0.059	0.022	0.0	1	100	0.730	0.015	4.964	4.965	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.636	0.073	0.022	0.0	-1	100	0.752	0.019	4.837	4.838	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.617	0.058	0.022	0.0	1	5	0.738	0.016	4.898	4.899	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.812	0.073	0.021	0.0	1	1	0.740	0.010	5.153	5.154	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.016	0.0	-1	1	0.005	0.003	0.216	0.253	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.030	0.0	-1	5	0.003	0.002	0.191	0.252	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.030	0.0	1	100	0.001	0.001	0.454	0.644	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	-1	100	0.001	0.000	0.584	0.615	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	1	5	0.001	0.000	0.652	0.653	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	1	1	0.001	0.000	0.675	0.675	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.858	1.065	0.000	0.001	-1	1	0.113	0.002	7.595	7.596	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	0.000	0.002	-1	1	0.000	0.000	8.157	8.706	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.062	0.455	0.000	0.001	-1	5	0.207	0.002	5.128	5.129	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	5	0.000	0.000	7.583	8.002	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.643	0.611	0.000	0.006	1	100	0.612	0.006	9.224	9.224	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	1	100	0.001	0.000	4.083	4.296	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.248	0.218	0.000	0.003	-1	100	0.634	0.008	5.122	5.122	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	0.000	0.005	-1	100	0.001	0.000	6.911	7.337	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.745	0.412	0.000	0.002	1	5	0.219	0.005	7.959	7.961	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	0.000	0.002	1	5	0.000	0.000	3.713	3.955	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.955	0.327	0.000	0.001	1	1	0.112	0.002	8.540	8.541	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	3.529	3.764	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.027	0.015	0.001	0.000	-1	1	0.000	0.000	67.088	68.418	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	1	0.000	0.000	28.925	30.195	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.023	0.001	0.001	0.000	-1	5	0.001	0.000	36.541	36.567	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	24.348	25.237	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.038	0.013	0.000	0.000	1	100	0.004	0.000	8.452	8.452	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	5.661	6.133	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.040	0.008	0.000	0.000	-1	100	0.005	0.001	7.427	7.612	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	100	0.000	0.000	19.893	21.333	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.020	0.000	0.001	0.000	1	5	0.001	0.000	29.106	29.960	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	6.114	6.446	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.019	0.000	0.001	0.000	1	1	0.000	0.000	51.293	51.565	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	6.265	6.600	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.512	0.082	30	0.031	0.0	random	0.379	0.028	1.350	1.354	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.554	0.016	30	0.029	0.0	k-means++	0.408	0.026	1.355	1.358	See	See
6	KMeans_tall	fit	1000000	1000000	100	5.681	0.161	30	0.141	0.0	random	2.795	0.047	2.033	2.033	See	See
9	KMeans_tall	fit	1000000	1000000	100	5.886	0.046	30	0.136	0.0	k-means++	2.941	0.024	2.001	2.001	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.010	0.000	random	0.0	0.0	9.477	13.978	See	See
2	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	random	0.0	0.0	8.921	13.921	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.010	0.000	k-means++	0.0	0.0	12.671	14.320	See	See
5	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	k-means++	0.0	0.0	13.437	14.131	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.475	0.000	random	0.0	0.0	6.740	7.236	See	See
8	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	random	0.0	0.0	13.358	14.048	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.496	0.000	k-means++	0.0	0.0	6.416	6.847	See	See
11	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	k-means++	0.0	0.0	12.651	13.231	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

WARNING! Mismatch between validation scores for 4 predictions (50.0%). See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	adjusted_rand_score_sklearn	adjusted_rand_score_sklearnex	diff_adjusted_rand_scores
1	KMeans_short	predict	10000	1000	2	0.000126	-0.000965	0.001090
4	KMeans_short	predict	10000	1000	2	0.001245	-0.000750	0.001995
7	KMeans_short	predict	10000	1000	100	0.278733	0.293767	0.015034
10	KMeans_short	predict	10000	1000	100	0.317011	0.256968	0.060044

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.072	0.000	20	0.002	0.0	random	0.027	0.004	2.668	2.703	See	See
3	KMeans_short	fit	10000	10000	2	0.210	0.002	20	0.001	0.0	k-means++	0.080	0.000	2.619	2.619	See	See
6	KMeans_short	fit	10000	10000	100	0.190	0.002	20	0.042	0.0	random	0.105	0.001	1.803	1.803	See	See
9	KMeans_short	fit	10000	10000	100	0.566	0.008	20	0.014	0.0	k-means++	0.290	0.002	1.954	1.954	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	random	0.000	0.0	3.897	3.918	See	See
2	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	random	0.000	0.0	12.909	13.248	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	k-means++	0.000	0.0	3.989	4.004	See	See
5	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	k-means++	0.000	0.0	13.686	14.135	See	See
7	KMeans_short	predict	10000	1000	100	0.002	0.0	20	0.331	0.000	random	0.001	0.0	2.523	2.531	See	See
8	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	random	0.000	0.0	10.753	10.884	See	See
10	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.313	0.000	k-means++	0.001	0.0	2.735	2.742	See	See
11	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	k-means++	0.000	0.0	9.549	9.665	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

WARNING! Mismatch between validation scores for 2 predictions (50.0%). See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	LogisticRegression	predict	1000000	1000	100	0.56	0.55	0.01
4	LogisticRegression	predict	1000	100	10000	0.35	0.28	0.07

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	11.193	0.452	[20]	0.071	0.000	2.000	0.030	5.596	5.597	See	See
3	LogisticRegression	fit	1000	1000	10000	1.058	0.896	[26]	0.076	0.001	0.915	0.033	1.155	1.156	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[20]	1.911	0.0	0.001	0.001	0.694	1.194	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[20]	0.017	0.0	0.000	0.000	0.349	0.356	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[26]	4.897	0.0	0.004	0.001	0.459	0.496	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[26]	1.182	0.0	0.001	0.000	0.100	0.101	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

WARNING! Mismatch between validation scores for 1 prediction (25.0%). See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	r2_score_sklearn	r2_score_sklearnex	diff_r2_scores
1	Ridge	predict	1000	1000	10000	0.082567	0.122191	0.039624

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.177	0.004	0.453	0.0	0.185	0.002	0.954	0.955	See	See
3	Ridge	fit	1000000	1000000	100	1.143	0.054	0.700	0.0	0.314	0.271	3.638	4.804	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.012	0.0	6.890	0.0	0.019	0.0	0.600	0.600	See	See
2	Ridge	predict	1000	1	10000	0.000	0.0	1.398	0.0	0.000	0.0	0.598	0.647	See	See
4	Ridge	predict	1000000	1000	100	0.000	0.0	5.448	0.0	0.000	0.0	0.459	0.712	See	See
5	Ridge	predict	1000000	1	100	0.000	0.0	0.017	0.0	0.000	0.0	0.584	0.616	See	See